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Digital & Intelligence Engineering · Efficient intelligence at scale

AI Inference, GPU & Model Cost Optimization

Make AI products faster, smaller, and economically viable at production volume.

Compact AI models moving through optimized GPU serving paths with latency and cost signals
Efficient inference by design

The work

Engineering that holds up beyond the demo.

We help enterprises and AI-enabled product companies reduce the cost and latency of model inference without losing the quality their users and workflows depend on. We benchmark real workloads, optimize models and runtimes, improve accelerator utilization, and design cloud, private-cloud, edge, and on-device serving strategies around measurable service-level and unit-economic targets.

What we help you achieve

  • Lower cost per request, user, workflow, or product transaction
  • Faster and more predictable response times at production load
  • Higher GPU and accelerator utilization with clearer capacity planning
  • A measured quality-versus-cost tradeoff instead of blind model reduction
  • A repeatable path from benchmark to optimized serving and operations

Detailed capabilities

The work behind a dependable platform.

Explore the engineering areas that take this capability from a first technical decision to a stable, production-ready system.

01

Inference Economics & Workload Baseline

Optimization starts with a credible view of what the workload costs and where time and capacity are being consumed.

  • Profile request shapes, context length, concurrency, traffic patterns, model mix, user journeys, and business-critical paths
  • Measure time to first token, end-to-end latency, throughput, queueing, memory, accelerator utilization, availability, and cost
  • Build cost-per-request, cost-per-user, cost-per-transaction, and capacity models tied to realistic growth scenarios
  • Define quality, latency, throughput, availability, and cost targets with explicit tradeoff boundaries
02

Model Compression & Runtime Optimization

Smaller and faster inference comes from coordinated model, runtime, and hardware decisions.

  • Apply quantization, distillation, pruning, sparsity, compilation, low-rank adaptation, and model routing
  • Benchmark quality regression across representative tasks, long-tail cases, safety checks, and product workflows
  • Tune tokenization, context budgets, batching, KV cache, speculative decoding, caching, and streaming
  • Select runtimes and serving stacks around model family, accelerator, memory, throughput, and operational constraints
03

GPU & Accelerator Utilization

Capacity is an engineering system: scheduling, queueing, memory, placement, and traffic shape determine economics.

  • Optimize GPU, CPU, NPU, memory, queue, batch, and accelerator utilization across training and inference pools
  • Design quotas, priority, autoscaling, bin-packing, multi-tenancy, isolation, and workload placement policies
  • Evaluate cloud GPU choices, reserved capacity, spot strategies, private infrastructure, and hybrid failover
  • Create utilization dashboards, capacity plans, saturation alerts, and cost-allocation views
04

Serving Architecture & Product Experience

The serving layer should make optimized inference visible in the product without hiding failure or uncertainty.

  • Build gateways, routing, fallbacks, batching, caching, streaming, rate limits, and graceful degradation
  • Integrate inference with identity, entitlements, tenant isolation, telemetry, user feedback, and human escalation
  • Select cloud, private-cloud, edge, mobile, or device deployment based on latency, residency, connectivity, and privacy
  • Validate product behavior under peak load, partial failure, model fallback, and degraded network conditions
05

Optimization Operations & Continuous Improvement

Cost and performance regressions need the same release discipline as model quality and security changes.

  • Monitor quality, latency, throughput, availability, accelerator use, memory, token volume, and spend
  • Track model, runtime, prompt, routing, hardware, configuration, and traffic changes against baseline
  • Use canaries, shadow traffic, A/B tests, rollback, capacity triggers, and optimization backlogs
  • Establish ownership, SLOs, incident playbooks, budget thresholds, and recurring economics reviews

Technical scope

Engineering detail from reset to runtime.

The work is designed around observable system behavior: what executes first, how resources are controlled, how failures are recovered, and how the platform proves it is ready.

01

Benchmark & economics

  • Request shape, context, concurrency, traffic, latency, throughput, queueing, memory, availability, quality, cost-per-request, and growth modeling
  • Targets and tradeoffs across accuracy, groundedness, safety, time to first token, end-to-end latency, throughput, and unit economics
02

Model & runtime optimization

  • Quantization, distillation, pruning, sparsity, compilation, routing, context budgets, KV cache, speculative decoding, caching, batching, and streaming
  • Quality regression, long-tail task coverage, safety checks, runtime selection, accelerator compatibility, and reproducible benchmarks
03

Accelerator platform

  • GPU, CPU, NPU, memory, queue, batch, scheduling, quotas, priority, autoscaling, bin-packing, multi-tenancy, isolation, and placement
  • Cloud capacity, private infrastructure, reserved and spot economics, hybrid failover, utilization, allocation, and capacity planning
04

Serving operations

  • Gateways, model routing, fallback, rate limits, tenant isolation, identity, entitlements, telemetry, user feedback, and human escalation
  • Quality, latency, throughput, availability, token, resource, spend, change, canary, shadow, A/B, rollback, SLO, and budget monitoring

Services and customer value

Technical work with a business reason.

Every engineering activity is tied to a decision your team needs to make: bring up faster, reduce risk, improve determinism, or prove production readiness.

01

Inference economics baseline

Technical work

Profile real workloads, establish quality, latency, throughput, capacity, and cost baselines, then model improvement opportunities against growth scenarios.

Customer benefit

Leaders see where AI economics are being lost and which interventions can improve unit cost without guessing.

02

Model compression and runtime optimization

Technical work

Apply quantization, distillation, pruning, compilation, routing, caching, batching, context, and serving optimizations with quality regression testing.

Customer benefit

Products get faster and more affordable while preserving the behavior users and workflows actually need.

03

GPU and accelerator optimization

Technical work

Improve scheduling, utilization, memory, queueing, autoscaling, workload placement, quotas, capacity planning, and cloud-versus-private economics.

Customer benefit

The organization gets more useful work from its accelerator budget and clearer decisions about the next capacity investment.

04

Production serving architecture

Technical work

Engineer gateways, routing, fallbacks, rate limits, tenant isolation, streaming, observability, and cloud, hybrid, edge, or device deployment.

Customer benefit

AI remains responsive and resilient when demand spikes, a model fails, connectivity changes, or a product needs controlled degradation.

05

Continuous AI cost and performance operations

Technical work

Connect quality, performance, resource, spend, release, canary, rollback, SLO, budget, and optimization signals into an operating loop.

Customer benefit

Cost optimization becomes a managed engineering capability rather than a one-time tuning exercise.

Fixed-bid project options

Defined scope. Measurable outcomes.

Start with a clearly bounded enterprise engineering program. Each package defines the work, outputs, assumptions, and acceptance criteria before implementation begins.

01Assessment

AI Inference Economics Assessment

Baseline one defined model workload or product journey across quality, latency, throughput, accelerator use, capacity, and cost.

Deliverables

  • Workload profile and production benchmark plan
  • Quality, latency, throughput, capacity, and cost baseline
  • Prioritized optimization opportunity map
  • Target-state economics and implementation roadmap

Acceptance criteria

The agreed workload has reproducible baseline measurements, a documented cost model, and prioritized interventions tied to target outcomes.

02Optimization sprint

Model & Runtime Optimization Sprint

Optimize one agreed model and serving path using compression, runtime, routing, batching, caching, or context interventions.

Deliverables

  • Optimized model or runtime configuration
  • Before-and-after benchmark and quality report
  • Deployment artifacts and rollback configuration
  • Operational recommendations and known limitations

Acceptance criteria

The optimized path meets the agreed quality threshold and demonstrates the agreed latency, throughput, or cost improvement against the baseline.

03Architecture program

GPU Capacity & Serving Blueprint

Design the accelerator, serving, scheduling, capacity, and economics strategy for a defined portfolio or product scale target.

Deliverables

  • GPU and accelerator capacity model
  • Serving, scheduling, autoscaling, and isolation architecture
  • Cloud, private, hybrid, edge, and device comparison
  • SLO, cost, monitoring, and rollout blueprint

Acceptance criteria

Engineering and finance stakeholders approve the target architecture, capacity assumptions, SLOs, cost model, and implementation sequence.

Why this matters for AI

Intelligence needs a dependable execution layer.

AI economics are part of product architecture. We connect model behavior, runtime choices, accelerator capacity, serving design, user experience, and financial outcomes so enterprises and AI-enabled product companies can scale intelligence without allowing inference cost or latency to erase its value.

What you take forward

Clear outputs your team can build on.

  • Inference workload profile, benchmark harness, quality and performance baseline, cost model, capacity assumptions, and optimization roadmap
  • Optimized model and runtime package with compression, routing, context, caching, batching, streaming, deployment, and rollback configuration
  • GPU and accelerator utilization package with scheduling, quotas, autoscaling, workload placement, capacity, and cost allocation
  • Production serving handoff with gateways, identity, tenant isolation, fallback, telemetry, SLOs, and incident runbooks
  • Continuous optimization operating model with quality, latency, throughput, resource, spend, release, budget, and improvement reviews

Capability map

Built across the layers.

The right answer is usually a connected system of decisions, not a single isolated skill.

01

Inference benchmarking, workload characterization, and cost-per-request modeling

02

Quantization, distillation, pruning, sparsity, compilation, and model routing

03

GPU, CPU, NPU, memory, queue, batch, and accelerator-utilization optimization

04

vLLM, Triton, Kubernetes, gateway, caching, batching, and autoscaling patterns

05

Cloud, private-cloud, hybrid, edge, mobile, and on-device serving architectures

06

Latency, throughput, availability, quality, and cost observability

07

Capacity planning, rollout, fallback, rollback, and continuous optimization

Keep exploring

See how the pieces connect.